Bloom Filter with Noisy Coding Framework for Multi-Set Membership Testing

Dai, Haipeng; Yu, Jingrong; Li, Meng; Wang, Wei; Liu, Alex X.; Ma, Jinghao; Qi, Lianyong; Chen, Guihai

doi:10.1109/tkde.2022.3199646

Cited by 52 publications

(28 citation statements)

References 47 publications

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“…This process is repeated 30 times to alleviate potential sampling bias. Based on these predictive values, this article uses Wilcoxon's signed‐rank test 44 with a Bonferroni correction 50,51 to check whether the prediction performance of two methods has a significant difference. Wilcoxon's signed‐rank test is a nonparametric statistical test, and the Bonferroni method is used to correct the

p

‐value.…”

Section: Resultsmentioning

confidence: 99%

Multi‐task deep neural networks for just‐in‐time software defect prediction on mobile apps

Huang

2023

Concurrency and Computation

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SummaryWith the development of smartphones, mobile applications play an irreplaceable role in our daily life, which characteristics often commit code changes to meet new requirements. This characteristic can introduce defects into the software. To provide immediate feedback to developers, previous researchers began to focus on just‐in‐time (JIT) software defect prediction techniques. JIT defect prediction aims to determine whether code commits will introduce defects into the software. It contains two scenarios, within‐project JIT defect prediction and cross‐project JIT defect prediction. Regardless of whether within‐project JIT defect prediction or cross‐project JIT defect prediction all need to have enough labeled data (within‐project JIT defect prediction assumes that have plenty of labeled data from the same project, while cross‐project JIT defect prediction assumes that have sufficient labeled data from source projects). However, in practice, both the source and target projects may only have limited labeled data. We propose the MTL‐DNN method based on multi‐task learning to solve this question. This method contains the data preprocessing layer, input layer, shared layers, task‐specific layers, and output layer. Where the common features of multiple related tasks are learned by sharing layers, and the unique features of each task are learned by the task‐specific layers. For verifying the effectiveness of the MTL‐DNN approach, we evaluate our method on 15 Android mobile apps. The experimental results show that our method significantly outperforms the state‐of‐the‐art single‐task deep learning and classical machine learning methods. This result shows that the MTL‐DNN method can effectively solve the problem of insufficient labeled training data for source and target projects.

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p

‐value.…”

Section: Resultsmentioning

confidence: 99%

Multi‐task deep neural networks for just‐in‐time software defect prediction on mobile apps

Huang

2023

Concurrency and Computation

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show abstract

“…For a user u tar in the left half of Figure 4, his behavior sequence of all time slices can be expressed as BS u tar (T) = {bs u tar (1), bs u tar (2) Additionally, we normalize the rating system of different platforms using the half triangular membership function. 52 As shown in Formula (5), R(u tar , i j , p k ) represents the rating of the item i j made by the user u tar on the platform p k .…”

Section: The Relationship Prediction Processmentioning

confidence: 99%

“…Additionally, we normalize the rating system of different platforms using the half triangular membership function 52 . As shown in Formula (5),

R\left({u}_{tar},{i}_j,{p}_k\right)

represents the rating of the item

{i}_j

made by the user

{u}_{tar}

on the platform

{p}_k

.…”

Section: Relationship Prediction Approach: Cpsrpmentioning

confidence: 99%

Microservice‐driven privacy‐aware cross‐platform social relationship prediction based on sequential information

Shen

Khan

et al. 2023

Softw Pract Exp

Self Cite

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Currently, the accurate prediction of social relationships can effectively reduce the decision‐making burden of users in various service platforms. However, in the big data environment, the users' data information used for the relationship prediction is highly fragmented distribution, so it is a non‐trivial challenge to integrate the users' sequence data information from different platforms while preventing sensitive information leakage. To this end, based on the microservice environment, we devise a cross‐platform social relationship prediction approach (CPSRP) to address the above problems. Briefly, the improved Simhash method aggregates similar users into the common bucket. Then the embedding technique converts the users' sparse data information into the low‐dimensional dense continuous feature vectors; the redefined Gated Recurrent Unit (r‐GRU) network and the Multilayer Perceptron (MLP) network are employed to extract the overall temporal sequence features of users. The relationship prediction is finally executed according to the users' sequential features. Extensive experiments are conducted on Epinions, and the experimental results further prove the benefits of our proposal in terms of relationship prediction while protecting users' sensitive information.

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“…However, it was not until the 1980s that digital animation started to gain traction in the mainstream, driven primarily by advancements in computer technology and software. 9,10 The introduction of computer generated imagery (CGI) opened a new realm of possibilities for animators. The first significant use of CGI in films was in the 1982 movie "Tron."…”

Section: Historical Overview Of Animationmentioning

confidence: 99%

“…The advent of computers led to a paradigm shift in animation, marking the transition from traditional to digital. The 1960s and 1970s saw the first experiments with computer animation, notably in works like Ivan Sutherland's “Sketchpad” and John Whitney's “Catalog.” However, it was not until the 1980s that digital animation started to gain traction in the mainstream, driven primarily by advancements in computer technology and software 9,10 . The introduction of computer generated imagery (CGI) opened a new realm of possibilities for animators.…”

Section: Historical Overview Of Animationmentioning

confidence: 99%

Evolution and innovations in animation: A comprehensive review and future directions

Wang,

Zhong

2023

Concurrency and Computation

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SummaryIn this article, we offer an in‐depth exploration of the history, advancements, and innovations in the field of animation. From the roots of traditional hand‐drawn animation to the contemporary realm of computer‐generated imagery, the article outlines the progression of techniques and technologies that have revolutionized the industry. This study meticulously examines the transformative impact of emerging trends such as virtual reality, augmented reality, and artificial intelligence on the development and application of animation. A significant focus is placed on the societal implications of animation, investigating its pervasive influence in fields as diverse as education, advertising, and entertainment. The use of animation as a pedagogical tool, a marketing strategy, and a medium for storytelling has not only redefined these sectors but has also reshaped audience expectations and experiences. In forecasting the future of animation, the article identifies and analyzes potential technologies and trends poised to push the boundaries of the field. This includes examining challenges such as the ethical considerations of AI and the technical constraints of VR and AR. By providing a comprehensive overview of past developments and future possibilities, this review contributes to the ongoing discourse on the continually evolving landscape of animation.

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Bloom Filter with Noisy Coding Framework for Multi-Set Membership Testing

Cited by 52 publications

References 47 publications

Multi‐task deep neural networks for just‐in‐time software defect prediction on mobile apps

Multi‐task deep neural networks for just‐in‐time software defect prediction on mobile apps

Microservice‐driven privacy‐aware cross‐platform social relationship prediction based on sequential information

Evolution and innovations in animation: A comprehensive review and future directions

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